class_docstr: Convolution-augmented Emformer layer that constitutes ConvEmformer.
  
  Args:
      input_dim (int): input dimension.
      num_heads (int): number of attention heads.
      ffn_dim: (int): hidden layer dimension of feedforward network.
      segment_length (int): length of each input segment.
      kernel_size (int): size of kernel to use in convolution module.
      dropout (float, optional): dropout probability. (Default: 0.0)
      ffn_activation (str, optional): activation function to use in feedforward network.
          Must be one of ("relu", "gelu", "silu"). (Default: "relu")
      left_context_length (int, optional): length of left context. (Default: 0)
      right_context_length (int, optional): length of right context. (Default: 0)
      max_memory_size (int, optional): maximum number of memory elements to use. (Default: 0)
      weight_init_gain (float or None, optional): scale factor to apply when initializing
          attention module parameters. (Default: ``None``)
      tanh_on_mem (bool, optional): if ``True``, applies tanh to memory elements. (Default: ``False``)
      negative_inf (float, optional): value to use for negative infinity in attention weights. (Default: -1e8)
      conv_activation (str, optional): activation function to use in convolution module.
          Must be one of ("relu", "gelu", "silu"). (Default: "silu")
code: def _apply_post_attention(
      self,
      rc_output: torch.Tensor,
      ffn0_out: torch.Tensor,
      conv_cache: Optional[torch.Tensor],
      rc_length: int,
      utterance_length: int,
  ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
      result = (
          self.dropout(rc_output)
          + ffn0_out[: rc_length + utterance_length]
      )
      conv_utterance, conv_right_context, conv_cache = self.conv(
          result[rc_length:], result[:rc_length], conv_cache
      )
      result = torch.cat([conv_right_context, conv_utterance])
      result = self.ffn1(result)
      result = self.layer_norm_output(result)
      output_utterance, output_right_context = (
          result[rc_length:],
          result[:rc_length],
      )
      return output_utterance, output_right_context, conv_cache
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  from torchaudio.models.emformer import _EmformerAttention, _EmformerImpl, _get_weight_init_gains
label_desc: Use image node_id 6 for calling the _ConvEmformerLayer obj's underlying member method code with example usage: obj._apply_post_attention(rc_output, ffn0_out, conv_cache, rc_length, utterance_length) and returns: output_utterance,  output_right_context,  conv_cache